Method and apparatus for automated quality assurance in medical imaging

ABSTRACT

The present invention relates to a computer-implemented quality assurance system, which includes the steps of retrieving quality assurance and supporting information from a database; receiving information on technical variables from monitoring of the patient, and on radiographic equipment in the performance of an imaging study; generating a quality assurance score after said imaging study based on said technical variables and said quality assurance and supporting information; and performing a quality assurance analysis of the imaging study based on the quality assurance score.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional PatentApplication No. 60/675,445, dated Apr. 28, 2005, the contents of whichare herein incorporated by reference in their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a quality assurance (QA) program thatcan be used in any medical discipline, and in particular in the area ofradiology. The present invention is a description of a computer-basedsoftware program that converts a manual QA process into an automatedone. In the process of doing so, a number of objective data arecollected for real-time and future analysis, thereby providing objectivefeedback to all practitioners for continuing quality improvement. Thepresent invention described, transcends the single process of imageacquisition alone, and carries over to the additional processes of imagedisplay, interpretation, and reporting. In the end, it is intended toimprove patient safety and overall clinical outcomes.

2. Description of the Related Art

The first and foremost priority for any QA program is improved qualityof service, and for any medical discipline the ultimate goal is improvedpatient care. This means that the product or service offers amethodology and the means with which to enhance patient diagnosis and/ortreatment, and in doing so objectively improves overall health outcomes.

In the case of medical imaging, the primary focus of attention is themedical imaging exam itself, which provides the means with which to makea diagnosis and initiate or adjust clinical management.

For the last 100 years, the end-product of medical imaging was ahard-copy film, which was displayed on a view box by the radiologistand/or clinician. However, in the past decade, medical imaging hasundergone a fundamental transition to digital imaging technologies,which capture, archive, transfer, and display medical imaging studies oncomputers. This affords the opportunity for medical imagingprofessionals to leverage the enhanced capabilities of computers toautomate what was previously a manual process and utilize computertechnologies to manipulate the image in a manner that accentuatescertain radiologic features to enhance clinical diagnosis.

While the digital transition has expanded the sophistication oftechnologies available to medical imaging practitioners, most (if notall) providers still operate in a “film based” workflow model. Thismeans that technologists (who acquire the images), radiologists (whointerpret the images), administrators (who are responsible for resourceallocation), and vendors (who provide the imaging and informationtechnologies), all maintain a QA focus which is outdated and reflectiveof the more traditional film-based mode of operation. For all intent andpurposes, the end-product (medical image) is still thought of andprocessed in a manner which emulates film, thereby obviating many of thepotential advantages of a filmless operation.

Accordingly, the ability to automate what was previously a manualprocess and use the inherent intelligence and consistency of computersto objectively perform a variety of functions to enhance datacollection, analysis, and feedback, is needed.

SUMMARY OF THE INVENTION

The present invention relates to a computer-based software program thatprovides an automated QA process for radiology; although one of ordinaryskill in the art would recognize that this invention could be applied toother medical disciplines as well as non-medical disciplines.

In the present invention, a number of objective data are collected forreal-time and future analysis, thereby providing objective feedback toall users for continuing quality improvement. The present invention asdescribed, transcends the single process of image acquisition alone, andcarries over to the additional processes of image display,interpretation, and reporting. In the end, it is intended to improvepatient safety and overall clinical outcomes.

In one embodiment, a computer-implemented quality assurance system forradiology, includes the steps of retrieving quality assurance andsupporting information from a database; receiving information ontechnical variables from radiographic equipment in the performance of animaging study; generating a quality assurance score after the imagingstudy based on the technical variables and the quality assurance andsupporting information; and performing a quality assurance analysis ofthe imaging study based on the quality assurance score.

In one embodiment, the system further includes correlating informationreceived from the technical variables with the quality assurance andsupporting information from the database; and performing a trendinganalysis. The trending analysis is used to generate recommendations foreducation and providing feedback to users.

In one embodiment, quantitative motion detection index (QMDI) scores aregenerated from information on the technical variables; and a qualityassurance score is calculated based on motion detection (i.e., motion,position, collimation).

In one embodiment, the system includes retrieving an ideal imaging studyfrom the database; performing a comparison of images from the idealimaging study and the imaging study, to determine a change inpositioning; calculating a positional quality assurance score; anddetermining a change in position required based on the positionalquality assurance score. The quality assurance score is based on aLikert scale of 1-4, wherein 1 is nondiagnostic, 2 is limited, 3 isdiagnostic, and 4 is exemplary.

In one embodiment, the system displays a warning to a user when saidquality assurance score is 1 or 2, and if so, the imaging study isrepeated.

In one embodiment, the system includes calculating a radiation doseexposure based on exposure parameters received from the technicalvariables. In addition, exposure parameters are correlated withclinician and patient specific variables to determine the qualityassurance score.

In one embodiment, the system includes retrieving quality assurance andexposure parameters from the database; and setting default exposureparameters therefrom for the imaging study.

In one embodiment, the system includes retrieving quality assurance andexposure parameters from the database; and using predetermined exposureparameters in the imaging study.

In one embodiment, the system performs a peak signal to noise ratio(PSNR) analysis on the technical variables.

In one embodiment, the system includes cloning an image obtained fromthe imaging study; performing a shift and subtract operation on theimage; performing quantitative analysis on the image; and producing afinal quality assurance composite image.

In one embodiment the system includes generating an artifacts overlayfor the image.

In one embodiment, the system includes retrieving quality assurance andexposure parameters from the database; and determining whether acumulative exposure exceeds a predetermined threshold.

In one embodiment, the system includes adjusting a display to userpreferences.

In one embodiment, the system includes embedding quality assurance datainto user workflow in order to generate quality control profiles onusers.

In one embodiment, the system includes creating a best practice templatefor adherence by users.

In one embodiment, the system includes performing monitoring andanalysis of department workflow and user quality assurance issues.

In one embodiment, the system includes receiving data from qualitycontrol phantoms and including said data into the technical variablesinformation.

In one embodiment, the system includes performing testing and analysisof the data; and displaying coded responses to a user for calibrationand/or repair based on results of the testing and analysis.

In one embodiment, the system includes generating an exposure indexadjustment based on the data.

Thus has been outlined, some features consistent with the presentinvention in order that the detailed description thereof that followsmay be better understood, and in order that the present contribution tothe art may be better appreciated. There are, of course, additionalfeatures consistent with the present invention that will be describedbelow and which will form the subject matter of the claims appendedhereto.

In this respect, before explaining at least one embodiment consistentwith the present invention in detail, it is to be understood that theinvention is not limited in its application to the details ofconstruction and to the arrangements of the components set forth in thefollowing description or illustrated in the drawings. Methods andapparatuses consistent with the present invention are capable of otherembodiments and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein, as well as the abstract included below, are for thepurpose of description and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe methods and apparatuses consistent with the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a quality assurance system forradiology according to one embodiment consistent with the presentinvention.

FIG. 2A is a flow chart of a quality assurance system with respect totechnical variables related to motion, according to one embodimentconsistent with the present invention.

FIG. 2B is a continuation of the flow chart of FIG. 2A.

FIG. 3 is a flow chart of a quality assurance system with respect totechnical variables related to radiation exposure, according to oneembodiment consistent with the present invention.

FIG. 4 is a flow chart of a quality assurance system with respect toradiologist and clinician inputs, according to one embodiment consistentwith the present invention.

FIG. 5 is a flow chart of a quality assurance system with respect toquality control of the radiographic devices, according to one embodimentconsistent with the present invention.

DESCRIPTION OF THE INVENTION

The present invention relates to a computer-based software program thatimplements a quality assurance (QA) process in a radiologicalenvironment.

In the exemplary embodiment of medical (radiological) applications, theQA system 100 of the present invention (see FIG. 1) is designed tointerface with existing information systems such as a HospitalInformation System (HIS) 10, a Radiology Information System (RIS) 20, aradiographic device 21 which uses, among others, a computed radiography(CR) cassette or direct radiography (DR) system, a CR/DR plate reader22, and a Picture Archiving and Communication System (PACS) 30, andconforms with the relevant standards, such as the Digital Imaging andCommunications in Medicine (DICOM) standard, DICOM Structured Reporting(SR) standard, or the Radiological Society of North America'sIntegrating the Healthcare Enterprise (IHE) initiative.

Thus, bi-directional communication between the quality assurance (QA)system 100 of the present invention and the information systems, such asthe HIS 10, RIS 20, radiographic device 21, CR/DR plate reader 22, andPACS 30, etc., allows the QA system 100 to retrieve information fromthese systems and update information therein and provide the desiredreport generated by the quality assurance system 100.

The QA system 100 of the present invention (see FIG. 1) includes aclient computer 101, such as a PC, which may or not be interfaced orintegrated with the PACS 30, and includes an imaging display device 102capable of providing high resolution of digital images in 2-D or 3-D,for example. However, if the image resolution can be sufficiently high,the client may be a mobile terminal, such as a mobile computing device,or a mobile data organizer (PDA), operated by the user accessing theprogram 110 remotely from the client (see FIG. 2).

Methods and systems consistent with the present invention are carriedout by providing an input means 104 (see FIG. 1), or user selectionmeans, including hot clickable icons etc., or selection buttons, in amenu, dialog box, or a roll-down window of an interface provided at theclient 101, and the user may input commands through a programmablestylus, keyboard, mouse, speech processing means, laser pointer, touchscreen, or other input means 104.

The input or selection means 104 may be constituted by a dedicated pieceof hardware or its functions may be executed by code instructionsexecuted on the client processor 106, involving the display unit 102 fordisplaying the selection window and a stylus or keyboard for entering aselection, for example.

However, input of the symbols or icons, by a user would preferably beaccomplished using a multi-functional, programmable stylus 104, whichcan not only be used to draw symbols onto the image, but can alsoaccomplish other tasks intrinsic to the image display, navigation,interpretation, and reporting processes that are superior to usingtraditional computer keyboard or mouse methods (both within the PACS andElectronic Medical Report (EMR)).

The client 101 typically includes a processor 106 as a client dataprocessing means, the processor including a central processing unit(CPU) 107 or parallel processor and an input/output (I/O) interface 108,a memory 109 with a program 110 having a data structure 111, allconnected by a bus 112. Further, the client 101 would include an inputdevice or means 104, a display 102, and may also include one or moresecondary storage devices 113. The bus 112 may be internal to the client101 and may include an adapter to a keyboard or input device 104 or mayinclude external connections.

The imaging display device 102 for the present invention is a highresolution touch screen computer monitor, which would allow images, suchas x-rays, to be readable clearly, easily and accurately. Alternatively,the imaging display device 102 can be other touch sensitive devicesincluding tablet, pocket PC, and plasma screens. The touch screen wouldbe pressure sensitive and responsive to the input of the stylus 104which would be used to write/draw directly onto the image displayingdevice 102.

In addition, high resolution goggles may be used to provide end userswith the ability to review images without the physical constraints of anexternal computer.

Note that with respect to the client system 101, the graphics userinterface is a client application written to run on existing computeroperating systems which may be ported to other personal computer (PC)software, personal digital assistants (PDAs), and cell phones, and anyother digital device that has a screen or visual component andappropriate storage capability.

The processor 106 at the client 101 may be internal or external thereto,and executes a program 110 adapted to predetermined operations. Theprocessor 106 has access to the memory 109 in which may be stored atleast one sequence of code instructions comprising the program 110 andthe data structure 111 for performing predetermined operations. Thememory 109 and program 110 may be located within the client 101 orexternal thereto.

Note that at times the system of the present invention is described asperforming a certain function. However, one of ordinary skill in the artwould know that the program 110 is what is performing the functionrather than the entity of the system itself.

The program 110 which runs the QA method and system of the presentinvention can include a separate program 110 code for performing adesired operation, or may be a plurality of modules performingsub-operations of an operation, or may be part of a single module of alarger program 110 providing the operation.

The processor 106 may be adapted to access and/or execute a plurality ofprograms 110 corresponding to a plurality of operations. An operationrendered by the program 110 may be, for example, supporting the userinterface, data mining functions, performing e-mail applications, etc.

The data structure 111 may include a plurality of entries, each entryincluding at least a first storage area that stores the databases orlibraries of image files, for example.

The storage device 113 stores at least one data file, such as imagefiles, text files, data files, audio, video files, etc., in providing aparticular operation. The data storage device as storage means 113, mayfor example, be a database, including a distributed database connectedvia a network, for example. The database can be a computer searchabledatabase and may be a relational database. The storage device may beconnected to the server 120 and/or the client 101, either directly orthrough a communication network, such as a LAN or WAN. An internalstorage device 113, or an external storage device 114 is optional, anddata may also be received via a network and directly processed.

In methods and system consistent with the present invention, the client101 may be connected to other clients 101 or servers 120, includingadministration, billing or other systems, via a communication link 116as a client communication means, using a communication end portspecified by an address or a port, and the communication link 116 mayinclude a mobile communication link, a switched circuit communicationlink, or may involve a network of data processing devices such as a LAN,WAN, the Internet, or combinations thereof. In particular, thecommunication link may be to e-mail systems, fax, telephone, wirelesscommunications systems such as pagers and cell phones, wireless PDA'sand other communication systems.

The communication link 116 may be an adapter unit capable to executevarious communications protocols in order to establish and maintaincommunication with the server 120, for example. The communication link116 may be constituted by a specialized piece of hardware or may berealized by a general CPU executing corresponding program 110instructions. The communication link 116 may be at least partiallyincluded in the processor 106 executing corresponding program 110instructions.

In one embodiment consistent with the present invention, if a server 120is used in a non-distributed environment, the server 120 would include aprocessor 121 having a CPU 122 or parallel processor which is a serverdata processing means, and an I/O interface 123, but may also beconstituted by a distributed CPU 122 including a plurality of individualprocessors 121 on one or a plurality of machines. The processor 121 ofthe server 120 may be a general data processing unit, but preferably adata processing unit with large resources (i.e., high processingcapabilities and a large memory for storing large amounts of data).

The server 120 also includes a memory 124 with program 125 having a datastructure 126 all connected by a bus 127. The bus 127 or similarconnection line can also consist of external connections, if the server120 is constituted by a distributed system. The server processor 121 mayhave access to a storage device 128 for storing preferably large numbersof program 110 s for providing various operations to the users.

The data structure 126 may include a plurality of entries, each entryincluding at least a first storage area which stores image files, forexample, but may also have alternative embodiments including thatassociated with other stored information as one of ordinary skill in theart would appreciate.

The server 120 may be a single unit or may be a distributed system of aplurality of servers 120 or data processing units, and may be shared bymultiple users in direct or indirect connection to each other. Theserver 120 performs at least one server program for a desired operation,which is required in serving a request from the client 101.

The communication link 129 from the server 120 is preferably adapted tocommunicate with a plurality of clients.

The present invention is implemented in software which can be providedin a client and server environment, or in a distributed system over acomputerized network across a number of client systems. Thus, in thepresent invention, a particular operation may be performed either at theclient or the server, at the edge of a network or at the center, orboth. Therefore, at either the client or the server, or both,corresponding programs for a desired operation/service are available.

In a client-server environment, at least one client and at least oneserver are each connected to a network 220 such as a Local Area Network(LAN), Wide Area Network (WAN), and/or the Internet, over acommunication link 116, 129. Further, even though the systems HIS 10 andRIS 20, radiographic device 21, CR/DR reader 22, and PACS 30 (ifseparate), for example, are shown as directly connected to the client101, it is known that these systems could be connected to the clientover a LAN, WAN, and/or the Internet via communication links.Interaction with users may be through secure and non-secure internetconnectivity. Thus, the steps in the methods consistent with the presentinvention are carried out at the client or at the server, or at both,the server (if used) being accessible by the client over for example,the Internet using a browser application or the like.

The client system 101 may include communications via a wireless serviceconnection. The server system 120 may include communications withnetwork/security features, via a wireless server, which connects to, forexample, voice recognition. However, one of ordinary skill in the artwould know that other systems may be included.

In another embodiment consistent with the present invention, the clientsystem may be a basic system, and the server may include all of thecomponents necessary to support the software platform of the presentinvention. Further, the present client-server system may be arrangedsuch that the client system can operate independently of the serversystem, but that the server system can be optionally connected. In theformer situation, additional modules would instead be connected to theclient system. In another embodiment consistent with the presentinvention, the client system and server system can be disposed in onesystem, rather being separated into two systems.

Although the above physical architecture has been described above asclient-side or server-side components, one of ordinary skill in the artwould know that the above components of the physical architecture may bein either client or server, or in a distributed environment.

Further, although the above-described features and processing operationsmay be realized by dedicated hardware, or may be realized as programsincluding code instructions executed on data processing units, it isfurther possible that parts of the above sequence of operations arecarried out in hardware, whereas other of the above processingoperations are carried out using software.

The underlying technology allows for replication to various other sites.Each new site can maintain “state” with its neighbors so that in theevent of a catastrophic failure, other server systems can continue tokeep the application running, and allow the system to load-balance theapplication geographically as required.

Further, although aspects of one implementation of the present inventionare described as being stored in memory, one of ordinary skill in theart will appreciate that all or part of the methods and systemsconsistent with the present invention may be stored on or read fromother computer-readable media, such as secondary storage devices, likehard disks, floppy disks, CD-ROM, a carrier wave received from a networksuch as the Internet, or other forms of ROM or RAM either currentlyknown or later developed. Further, although specific components of thesystem have been described, one skilled in the art will appreciate thatthe system suitable for use with the methods and systems consistent withthe present invention, may contain additional or different components.

Accordingly, in one embodiment consistent with the present invention,the QA system 100 and method as used in an exemplary radiology methodand system, includes a client computer 101 with image displaying device102, and an input device 104. An image study is taken of the patent by atechnologist based on a physician's instructions, using the radiographicdevice 21, and the cassette is read at plate reader 22, and the imagesforwarded to the PACS 30, and then to the computer imaging displaydevice 102 for display and storage. The images can be stored, analyzedand utilized in the QA method and system of the present invention, ordiscarded.

In particular, the following describes in more detail, the QA system andmethod of the present invention.

A. Technologist QA Data

Specifically, during the process of taking the medical image, thetechnologist will position the patient at an examination table for theradiographic device 21 to capture the radiographic images. Theradiographic device 21 will obtain certain QA data during the imagingstudy which is forwarded to the client 101 and recorded by the computerprogram 110 when the medical image is taken.

In one embodiment consistent with the present invention, QA for theradiographic process is directed to correcting for technicaldeficiencies occurring in the image acquisition portion of the imagingstudy. These technical deficiencies can be the result of patientnon-compliance (e.g., motion), technologist oversight (e.g.,positioning), or technology malfunction (e.g., artifacts).

In the end, the automated QA program 110 of the present invention isresponsible for identification and analysis of these technicaldeficiencies and provide immediate feedback to the various users (e.g.,technologists, administrators, radiologists).

The QA data which is obtained by the program 110 during the imageacquisition, includes the following technical variables: 1) motion, 2)positioning, 3) exposure, 4) artifacts, 5) collimation, and 6)supporting data—which are discussed in more detail below.

After the program 110 obtains data on these technical variables, thecomputer program 110 automatically calculates a score for each of theabove technical variables along with the supporting data which includespatient and exam-specific identifying data, for each imaging exam takenby the technologist.

The client 101 is programmed to score the technical QA variables listedabove, on a computer-generated Likert scale of 1-4, using the followingdefinitions for the scale:

A score of 1 is “non-diagnostic”. This means little or no clinicallyuseful (diagnostic) information is contained within the image obtained.Since the available information obtained during the examination of thepatient does not answer the primary clinical question (i.e., indicationfor the study), then by definition this requires that the imaging exambe repeated for appropriate diagnosis.

A score of 2 is “limited”. This means that the information containedwithin the image obtained is less than expected for a typicalexamination of this type; however, it is sufficient to answer theprimary clinical question. The requirement that this exam be repeated isnot absolute, but is preferred, in order to garner maximal diagnosticvalue.

A score of 3 is “diagnostic”. This means that the information containedwithin the image is representative of the broad spectrum of comparableimages, allowing for the patient's clinical status and compliance. Boththe primary clinical question posed, as well as ancillary information,can be garnered from the image for appropriate diagnosis.

4) A score of 4 is “exemplary”. This means that the informationcontained within the image and overall image quality serves as anexample that should be emulated as the “ideal” for that specific imagingstudy and patient population. As stated above, the QA score is affectedby the technical variables noted above of: 1) motion, 2) positioning, 3)exposure, 4) artifacts, 5) collimation, and 6) supporting data.

1) Motion

Motion is one of (if not the most) important QA limiting factor inmedical imaging, regardless of the imaging modality. Almost all imagingmodalities (with the exception of ultrasound) are acquired in a staticmode, thereby creating the opportunity for motion to adversely affectspatial resolution and in turn compromising diagnostic accuracy. Sincethe offending culprit is the patient, in one embodiment, motion sensors23 are positioned directly onto the patient in order to maintain thehighest accuracy (see below for further discussion).

For the portable intensive care unit (ICU) exam, minimal motion would becustomary, in light of the inability for the comatose patient tovoluntarily hold their breath and remain completely still while theexamination is being conducted. The more compliant ambulatory patient,on the other hand, would be able to stand still, follow verbal commands,and refrain from respiratory and voluntary motion during the course ofthe exposure.

As a result, what is considered to be diagnostic for these exams (interms of “motion”), is far different. The ICU portable chest image mayhave minor respiratory motion that produces minimal blurring of the lungparenchyma, but provides the ability to accurately detect the presenceor absence of pneumonia, which is the stated clinical indication.

As a result, this exam would receive a QA motion score of “3”(diagnostic), for example, from the computer program 110, for thistechnical variable alone (see FIG. 2A, steps S511, S513, and S519). Ifon the other hand, the degree of respiratory motion caused a slightlygreater degree of blurring that resulted in partial obscuration of thediaphragm and tip of an indwelling central venous catheter, the QAmotion score would now be calculated by the computer program 110, as a“2” (limited). With a score of “2”, the lung fields are shown withenough clarity such that they can be assessed for the presence orabsence of pneumonia, but other important anatomic regions and criticalstructures are partially obscured in the image.

The more extreme QA motion scores of “1” (non-diagnostic) and “4”(exemplary) would correspond to a portable ICU chest radiographic imagethat is completely obscured by motion, and the opposite image that iscompletely devoid of motion.

If the ICU portable chest radiograph was compared with a standingambulatory chest radiograph, the evaluation and scoring of the technicalQA variables by the computer program 110, would vary significantly,based on the different expectations in image quality for these twoexaminations which are obtained for the same anatomic regions andclinical indications.

In another example, the standing chest radiograph performed in thepost-operative ambulatory patient is also performed in the evaluation ofpneumonia. In this clinical setting and patient population, theexpectations for image quality are different, based on differences inpatient compliance, clinical status, and technique. The standing chestradiograph is performed on a wall-mounted Bucky unit, that provides foroverall improvement in image quality. At the same time, the patient iscapable of following the verbal commands of the technologist and cansuspend respiration in full inspiration, thereby maximizing lung volumeand visualization of potential lung parenchymal pathology (such aspneumonia). The ICU patient, on the other hand, could not controlbreathing and was in a supine, dependent position, resulting in poorlyinflated lung fields, prone to motion.

For this standing chest radiographic image, the QA expectations areobviously different. Minimal motion that blurs the lung parenchyma wouldin this instance receive a QA motion score of “2” (limited), due todifferent expectations for the different patients and techniquesemployed.

Accomplishing the detection of motion in the present invention, can beperformed in a number of ways.

In one embodiment consistent with the present invention, determiningmotion detection (and thus, determining the QA motion score) entails theplacement, by the technologist, of miniature transparent motion phantoms23 directly onto the patient skin surface prior to the imaging study.These motion phantoms 23 can take a number of different forms, with somepossible candidates listed below:

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These motion phantoms 23 would be positioned at multiple points on thepatient's skin surface within the field of view. The computer program110 would generate a quantitative motion detection index (QMDI) score(see FIG. 2A, step S503) based on movement, blurring, or contourirregularity of each phantom image, which is superimposed directly overthe medical image. The transparency of the phantom image would avoidhaving any important information contained within the medical image frombeing obscured during the image review and interpretation process.

These QMDI scores would then be correlated by the computer program 110with a number of variables—including the anatomic region being imaged,the modality, patient physical characteristics, and clinical indication,to determine a QA motion score (on the Likert scale above), andtherefore, to determine whether the quantified amount of motion isdeemed acceptable (see FIG. 2A, step S504).

For example, obtaining QMDI scores in clinical practice can beillustrated with mammography. Depending upon the patient's body habitus(i.e., breast size) and pathology being analyzed, the degree of“acceptable motion” may vary. If for example, the pathology beinganalyzed is a mass or density (which is more dependent on contrast andnot spatial resolution for detection), modest degrees of motion would bedeemed acceptable for diagnosis.

If instead, the pathology being evaluated is microcalcifications (whichare highly dependent upon spatial resolution), then relatively smallmeasures of motion will not be acceptable. In that case, the QMDI scoremay be calculated by the computer program 110 based on the motion of thephantom images 23 in view of the other variables (anatomic region,modality, etc.) (see steps S503, S504). For example, the QMDI scores arerecorded by the program 110 along with the exposure parameters, breastcompression (measured in Newtons), number and type of views, field ofview, and compression device utilized (along with post-image processingapplied). The QMDI score obtained is then automatically correlated bythe program 110, for example, to a QA motion score of “1”, or“non-diagnostic” which is displayed for the technologist (see stepsS514, S515). The technologist will then be directed by this score, torepeat taking the radiographic image (see step S516), with computerprogram-recommended technical modifications (such as increasing thedegree of breast compression, and using a smaller compression paddle andfield of view), which are recommended based on the QMDI score and QAdata detected. Further, existing decision support computer-aideddetection software, which is currently in everyday use, can be mapped tothe motion computation device (i.e., motion phantoms 23) to correlatethe QMDI score with the pathology in question.

In another embodiment consistent with the present invention, thedetection of motion during medical imaging would include usingnanotechnology in the form of microscopic motion detection sensors 24that can provide real-time (i.e., instantaneous) motion feedbackdirectly to the radiographic acquisition device 21 (see steps S502,S504, S510). If this nanotechnology motion sensor 24 is correlated withexisting gating technology (e.g., cardiac gating), then the acquisitiondevice 21 can be temporally directed by the computer program 110 as tothe exact moment of minimal physiologic and patient-induced motion, toinitiate image capture by the radiographic device 21. This technology isbest suited for complex cross-sectional imaging modalities (e.g., CT andMRI) which are evaluating anatomic regions and types of pathologyexquisitely sensitive to small amounts of motion, which precludeaccurate diagnosis.

Cardiac CT and MR angiography would be two examples of non-invasiveimaging modalities which are currently limited (in diagnostic efficacy)by extremely small amount of motion. In this example, the nanotechnologymotion sensors 23 would be directly placed over the anatomic regions ofinterest (e.g., right and left ventricles) and directly calibrated withthe CT detection sensors and EKG gating device to coordinaterespiratory, cardiac, and patient-induced motion. When the desiredcomponent of the cardiac cycle begins and external motion detection isminimal, as detected by the detection sensors 24 (see step S504), the CTacquisition would be activated by the computer program 110. The QMDIcalculated by the computer program 110 (see step S503), and displayedand stored in the database 113 (see step S511, S518, S520), can in turnbe incorporated into the interpretation process and into the report (bythe cardiologist or radiologist) to ensure quantitative measures ofcoronary arterial stenosis are accurate and not adversely affected by QAlimitations (see step S521).

In another embodiment consistent with the present invention, in additionto motion sensors 23 (in macroscopic and microscopic forms), anothertype of motion detection uses reflective devices 31. The reflectivedevice 31 (e.g., transparent mirror) is placed directly onto thepatient's anatomic region of interest and light from a laser on theimaging modality is reflected onto the mirror 31. The reflected light isin turn processed by a motion sensor 24 (using signal processinganalysis), and a microprocessor 106 with computer program 110, todetermine the magnitude and directionality of motion. This calculatedQMDI in turn, is entered into a comprehensive motion database 113 of theclient 101, that can be categorized by specific patient, modality,anatomic region, and clinical indication to correlate QMDIs withclinician image QA scores (see step S520). These scores (on the Likertscale) can then be used to determine clinically acceptable levels ofmotion relative to each exam type and clinical indication.

2) Positioning

With respect to the next technical variable—positioning—the evaluationof image quality is also dependent upon the positioning of the exam(i.e., portable, upright, standing) and patient factors (i.e., bodyhabitus, clinical status, compliance).

Expectations for image quality would obviously vary for a portableexamination performed on a comatose patient in the intensive care unit(ICU), as opposed to a standing chest radiograph for an ambulatory (andrelatively healthy) outpatient. Technical deficiencies related toposition are most frequently encountered with general radiographic andmammographic imaging studies.

In order to ensure proper positioning in an automated manner, thecomputer program 110 must correlate the current imaging exam with acomparable “ideal” exam (see FIG. 2A, step S505), which is containedwithin a digital database 113 of optimized images. Each individualexamination and the multiple images contained with the comprehensiveimage data set would be cross-referenced by the computer program 110with the “idealized” template images within the database 113. Usingcomputerized pattern recognition and segmentation, the computer program110 can then compare the two imaging datasets to determine ifpositioning of the individual images has been altered (see step S506),and to notify the technologist with a display or alarm (i.e., sound orvisual display) (see step S509).

One way to accomplish this would be to utilize the transparent motionsensors 23 previously described as localizers for anatomic positioning.These sensors 23 could be positioned at predetermined locations withinthe anatomic region of interest. If for example, these motionsensors/anatomic localizers 23 are positioned at the four corners andcenter of the field of view, the computer program 110 can simultaneouslycompute the motion index and spatial orientation of the desired anatomicregion. If a technologist is acquiring an AP chest radiograph, he/shewould position the sensors 23 over each shoulder and the inferolateralaspect of the 12^(th) anterior ribs. This would in effect define theperimeter of the image, with a fifth sensor 23 positioned over thexiphoid process denoting the center. Since all patients would in effecthave the same anatomic markers, reproducibility of this system would beconsistent from patient to patient and technologist to technologist.

If the same task was performed on a more complicated five-view cervicalspine radiographic exam, the same system of anatomic markers (twosensors 23 over each mandibular angle, two sensors 23 over the midaspect of each clavicle, and a single centrally positioned sensor 23over the thyroid cartilage (i.e. Adam's apple)) would be utilized. Thesesensors 23 would remain fixed in location for each of the five viewsincluding the AP (i.e., frontal), lateral, oblique, and open mouthviews. Since the standardized reference (ideal) images would have thesame anatomic reference markers, the computer program 110 would compareboth sets of images (see step S506) to ensure the appropriate region isincluded in its entirety, as well as the orientation of the anatomicstructures contained within the image are similar. The greatestpositional challenge for this type of exam would be the oblique views,which should have the intervertebral foramina (where the nerve rootsexit the spine) viewed en face (which creates the appearance of lookingdirectly into a longitudinal series of round holes). Using computerizedpattern recognition, the computer program 110 can analyze and record thedegree of concordance and create a quantifiable positional score,highlighting any areas of significant discordance (see step S507).

These individual and collective computerized “positional scores” wouldthen be displayed as a QA score (see step S511), and the QA score couldthen be recorded into the technical component of the QA database, and besupplemented by the radiologists' and clinicians' image quality scores(as they relate to position) (see the discussion below). If anindividual technologist or group of technologists has certain types ofexams shown to have positional deficiencies, then educational andtraining programs (e.g., in-services) could be targeted to thesespecific problem areas (see FIG. 2B, step S526). By providing objective(and automated) instantaneous feedback to the technologist at the timeof image acquisition, it is anticipated that patient delays, call backs,and exam retakes would be reduced, along with overall improvement inimage quality.

3) Exposure

The next technical QA variable to analyze is “exposure”, whichultimately affects both contrast and spatial resolution of the chestradiographic image. For this QA variable, anatomic region and clinicalindication take on even greater importance. Accurate detection of asmall lung nodule would be more dependent upon subtle differences incontrast resolution, while interstitial lung disease or a non-displacerib fracture would be more dependent upon subtle differences in spatialresolution. As a result, the technical QA exposure score must take intoaccount the clinical indication and anatomic region being evaluated.

Exposure parameters currently used in medical imaging are to a largeextent idiosyncratic and highly variable from one technologist and oneinstitution to another. In order to maximize signal to noise ratio(i.e., minimize image noise), and optimize image contrast, manytechnologists (and radiologists) elect to use higher mAs and kVpsettings, which result in higher radiation doses.

For general radiographic examinations, the current standard fordetermining exposure time utilizes a photo-timer, which automaticallyterminates the exposure once a certain radiation dose is registered by asensor. The problem with this technology is that it does not adequatelytake into account unique differences in patient body habitus, previoussurgery, or underlying pathology. The exposure is controlled at one of afew limited points by the use of these strategically based detectorswhich do not do an adequate job of anticipating the optimal exposurefactor for the entire patient.

If for example, a patient has severe emphysema (causing the lungs tobecome over-inflated), the photo-timer will prolong the appear “tooblack”. The opposite occurs when a patient has a large pleural effusion(causing one side of the thorax to be lighter than the other, sincefluid is denser than air). In this opposite scenario, the photo-timerwill prematurely terminate the exposure, thereby causing theradiographic exposure to be under-penetrated or “too light”. Similarexposure deficiencies occur when patients have excessive sizedifferences or surgical hardware present.

In one embodiment consistent with the present invention, the exposureparameters collected during the image study can be stored in thecomputer database 113, i.e., within the acquisition device 21 and DICOMheader. The client 101 can be programmed to automatically transfer theexposure parameters to the individual exam and QA database 113 (see FIG.3, step S600), and the computer program 110 can use these parameters toautomatically calculate radiation dose exposure (see step S607) based ona pre-defined formula for each exam type and anatomic region.

The purposes of recording this data are to: 1) calculate radiationexposure doses for each exam, where a patient-specific dose exposure canbe cumulatively tallied and monitored by the computer program 110 (whichis of increasing importance as more medical imaging is used forscreening purposes), and 2) correlate the exposure parameters with theoverall QA database 113 to optimize image quality and patient safety inthe following manner.

In one embodiment, the computer program 110 correlates the exposureparameters with similar exam types (from other patients) andcross-references the exposure parameters with corresponding imagequality scores (as reported by the interpreting radiologists) (see stepS608). Thus, computer-generated recommendations for “optimum” exposureparameters are produced and displayed (see step S608) for the user bythe program 110, for eventual QA scoring (see step S511).

In another embodiment, the exposure parameters can be correlated by thecomputer program 110 (see step S608), with a number of clinical andpatient specific variables to predict how variation in the exposureparameters can be further optimized. Patient and specific factors couldinclude body habitus, interval weight gain or loss, mobility, andcompliance. Clinical factors could include underlying medical conditionsand indications for performing the requested exam.

In one example, a patient is referred for a chest radiograph to evaluatechronic cough. By the user accessing additional data from the electronicmedical record (EMR), the program 110 displays the information that thepatient is morbidly obese, but has experienced significant (andunexplained) weight loss of 100 pounds over the past 6 months. Thepatient's past medical history is significant for a long smoking historyresulting in chronic obstructive pulmonary disease (COPD), which causedthe lungs to be hyper-inflated. The patient's obesity, recent weightloss, and underlying COPD all have important relevance to the selectionof optimized exposure parameters. By the user querying the QA database113 for relevant data, the computer program 110 can automaticallyprovide the technologist with recommended exposure parameters (see stepS608). The additional history of longstanding smoking and chronic coughis a predictor of lung cancer, which also provides valuable informationon specific image processing parameters to be used for improveddiagnosis of lung cancer.

The patient's historical medical imaging data can be accessed by theuser from data stored in the radiology information system (RIS) 20 andpicture archival and communication system (PACS) 30. This can provideprevious exposure parameters for the same patient and same (or similar)imaging exams, along with the corresponding image quality scores (seestep S600). If for example, a previous mammogram had an overall imagequality score of 95 (out of a possible 100), the computer program 110could present the technologist with the specific exposure parametersemployed at the time of the prior exam and automatically set this as thedefault exposure parameters for the current study (see step S601).

When a patient's cumulative radiation dose exposure has exceeded apredefined limit stored in the QA database of the computer, the computerprogram 110 can issue an alert (by display, e-mail message, and/oraudible alarm) to the referring clinician, radiologist, technologist,medical physicist and administrator (see step S604). (In addition, thecomputer program 110 can automatically prepare an e-mail message orletter that can be sent directly to the patient with a detailedexplanation of risks and alternatives (see step S605).) This informationcan in turn be used to supplement efforts for reducing radiationexposure by programming the computer to use specialized low doseprotocols (e.g., CT), advising the replacement of imaging exams withionizing radiation for those without (e.g., MRI in lieu of CT), andanalysis of future imaging exam requests (see step S605). This is ofparticular importance to oncology patients, who undergo frequent andrepetitive imaging studies during the course and immediately followingtheir cancer diagnosis and treatment.

In addition to recording exposure parameters for each imaging exam,specific protocol parameters are recorded in the database by thecomputer program 110, for the program 110 to cross-reference theseparameters with clinical data pertinent to the exam indication,laboratory data, and diagnosis. These protocol parameters are especiallyvaluable for the computer program 110 to cross-sectional imagingmodalities such as computed tomography (CT) and magnetic resonanceimaging (MRI).

For example, in the case of a patient who is being followed for a small1 cm right lung cancer, which has been treated with chemotherapy andradiation therapy—on prior exams, the cancer was best visualized using0.5 mm collimation using a high-resolution protocol. Knowing thisspecific protocol provides the optimum comparison can not only improvediagnostic accuracy, but also reduce radiation exposure by minimizingthe use of additional and unnecessary imaging sequences. In addition,the specific post-acquisition processing parameters previously employedcan be automatically applied by the computer program 110 to ensuredirect comparison with the historical comparison study (see step S522).

In one embodiment, the present invention would optimize exposureparameters and utilize a “pre-scan”, which consists of an ultra low doseexposure (approximately 1/20^(th) the customary dose of conventionalexposure parameters) (see step S603). This “pre-scan” would create apixel density map of the anatomic region imaged, which can be displayedand stored by the computer program 110. The computer program 110 wouldthen perform a histogram analysis of pixel density of the pixel densitymap to compute the optimum exposure parameters, taking into account bothimage quality and radiation dose. Because the pixel density map would bespecific to each individual patient, modality, anatomic region, andunderlying pathology, the calculated exposure parameters would beindividually optimized, can be displayed by the computer program 110(see step S608), and stored in the QA database for future reference (seestep S520). This pixel density map could then be retrieved by thetechnologist in the future when he/she is repeating the same type ofexam, thereby improving image quality, consistency, and workflow (seestep S600).

When calculating a computer-generated correction for the mAs and kVp,additional technologies could be employed “on the fly” to balance therequirement for optimized contrast resolution and dose reduction. Inanother embodiment consistent with the present invention, the computerprogram 110 could perform an existing peak analysis to noise ratio(PSNR) analysis (see step S606), which evaluates the variability inpixel data to quantitatively measure the amount of noise containedwithin the medical image. The computer program 110 can then perform ananalysis to minimize noise and maximize signal, in order to optimizeoverall image quality. Using this PSNR technology, the computer program110 can in turn, calculate the inflection point at which dose reductioncan be achieved without causing clinically significant degradation inimage quality (see step S607).

Additional factors that can go into the analysis performed by thecomputer program 110, would include the patient's radiation history(i.e., cumulative radiation dose), the clinical indication for the exam,and pre-existing medical history. For example, a patient undergoing achest CT exam to follow up treatment of a pre-existing lung cancer couldhave a study specifically targeting the documented site of cancer. Thecomputer can be programmed to correlate this study with previous CTexams and the pertinent medical history stored in the database. The“pre-scan” can be performed directly over the area of known tumor. Thepixel density map obtained over this pre-defined area of interest (seestep S603) can then be analyzed by the computer program 110 inconjunction with the PSNR analysis (see step S606) to produce acomputer-generated correction for mAs and kVp that provides the requireddiagnostic information at the lowest possible radiation dose to thepatient (see step S607). This strategy is particularly beneficial in thepediatric patient populations where cumulative radiation dose takes onheightened importance. Since traditional image processing is currentlyperformed “after the fact” with the purpose of accentuating pathologyand reducing noise within the medical image, the strategy of the presentinvention to optimize exposure during the image acquisition process isunique in that it is prospective in nature. The data collected isautomatically entered into a QA database 113 (see step S520) that can besearched, and which can be used for technologist education and training,development of decision support software, and patient outcomes analysis(see step S523).

In another embodiment consistent with the present invention, thecomputer can be programmed to apply a predetermined exposure to eachimage taken (see step S602). Since digital radiography provides medicalimaging with the ability to utilize computer-based tools and imageprocessing to enhance diagnosis, these decision support tools can becustomized to the end-users preferences, specific clinical indication,and anatomy being evaluated. These “user specific”, clinical, andanatomic profiles can in turn be archived in the computer database andautomatically applied (see steps S501, S502).

4) Artifacts

The next QA variable is artifacts. Artifacts represent signals withinthe detected and displayed image that are unwanted and degrade theability to make an accurate diagnosis. These signals arise from varioussources, including:

the x-ray detector (e.g., flat panel defects such as detector elementdropouts and column and row malfunction, scratches or debris on CRimaging plates, poor compensation for signal shading and flat-fieldcorrections);

b) components within the x-ray system projecting non-uniform or periodicsignals onto the detector (e.g., antiscatter grid—either non-moving orinappropriate motion during the exposure, debris such as contrast agenton the detector cover, high density materials on the x-ray tubefilter(s), inappropriate positioning of compensation filters to reducedynamic range);

c) the patient (e.g., involuntary or voluntary motion during theexposure, external high-density objects such as overlying EKG leads,earrings, necklaces and makeup).

Artifacts are a major impediment to correct diagnosis. QA scoring ofartifacts is to some extent dependent upon the exam type, patientprofile, the anatomy being evaluated, and clinical indication. Forexample, for the portable ICU chest radiograph, a number of artifactscan be identified, which are to some extent expected and unavoidable.These critically ill patients are often on multiple monitoring devices(e.g., EKG leads) that cannot be removed for the imaging exam. Whilethese partially obscure the underlying anatomy within the chest, it is alimitation that must be accommodated for to take the radiographic image.

If on the other hand, EKG leads or snaps were present on the stable,ambulatory patient obtaining a standing chest radiograph, these wouldnot be considered to be essential and could be temporarily removed toenhance image quality.

The QA technical artifact scores for these two different situationswould be calculated differently by the computer program 110, based onunique differences in the patient profiles and clinical status.

Another extraneous artifact that can be avoided would be that of a skinfold which often produces an artifactual line over the lateral chest. Iffor example, the portable chest radiograph is being performed toevaluate pneumonia, this artifactual line would not adversely affect theability to render diagnostic interpretation and would therefore receivea technical QA score of “2” (limited). If however, the clinicalindication is to evaluate recent placement of a central venous catheter,then this QA score would now change to a “1” (non-diagnostic) becausethe skin fold can mimic the radiographic appearance of a pneumothorax,which is a clinically significant complication of central lineplacement. While the artifacts would have the same appearance, theclinical indication would necessitate a repeat exam in one case but notthe other.

Detection and localization of image artifacts are crucial, and qualitycontrol procedures needed to identify, and determine the cause, andcorrect the artifact, is desired in order to achieve optical imagequality.

For artifacts due to x-ray detectors, one embodiment consistent with thepresent invention to automatically identify, determine the direction,and localize the extent to the artifacts present is, to perform auniform x-ray exposure, fully irradiating the detector to 50% of itsdynamic range, using a long x-ray tube to detector distance, a typicalexposure time (e.g., 50 ms), and appropriate kV and mA settings with nogrid.

The acquired image is then cloned by the computer program 110 (see FIG.3, step S609), and the two identical images are shifted by a number ofpixels with respect to each other in a specific direction by the program110 (e.g., four pixels in the row direction for the cloned image) (seestep S610). The computer program 110 would then subtract the two images(maintaining the negative and positive differences) of the operation andignoring the edge effects), and add an offset signal, yielding a “shiftand subtract” image (e.g., SS 4×0y) for a given shift direction andnumber of pixels (see step S610).

The “shift and subtract” operation by the computer program 110 yields adifference image which is displayed by the program 110 on the monitor(see step S610), and delineates artifacts by creating negative andpositive artifact signals with amplitudes greater than the subtractedimage noise variation, and at the same time reduces low frequencyvariations that are not likely to be important. Linear artifactsperpendicular to the shift direction will generate extensive artifactsignals, while linear artifacts parallel to the shift direction willgenerate only small difference signals at the periphery of the artifact.Contrast enhancement of the difference image by the computer program110, provides a quick qualitative check for the presence of artifactsfor a single shift and subtract operation. Note that some structures inthe direction of shift are self-cancelling; also, subtle artifactsdifficult to appreciate in the original image are now easier to detect.

Quantitative analysis is performed by the computer program 110 (see stepS611) evaluating the total image with adjacent small region of interest(ROI) boxes (e.g., 64×64 pixel or other area), calculating the StandardDeviation (SD), and encoding the values with a grayscale or color outputscale.

Multiple shift and subtract operations in horizontal, vertical, andcombined directions, by the computer program 110, fully map the extentand locations of the artifacts (see step S611). Summation of theindividual difference images using a common coordinate position by thecomputer program 110, produces a composite summation image with obviousand subtle artifacts clearly delineated (see step S612). Identificationof regions with significant artifacts can be recognized by the computerprogram 110 mapping the SD values from the ROI areas in a grayscale orcolor scale as an overlay on the composite image. The procedure isimplemented by the computer program 110 using an automated algorithm toproduce the final QC composite image and an overlay map of areas withartifact generating signals (see step S612).

Artifacts generated from other components, requires a uniform attenuator(e.g., 15 cm uniform plastic, of a size to cover the majority of thedetector area), antiscatter grid assembly, and typical patientacquisition geometry. A similar procedure as described above for imageprocessing and analysis (shift and subtract) is used for automatedanalysis of artifacts using a computer program 110 (with knowledge ofany intrinsic detector artifacts per the initial evaluation).Additionally, noise power spectrum analysis (see step S611) is appliedby the computer program 110 to the image, to determine specific spatialfrequencies with increased noise contributions, such as that produced bya non-reciprocating grid and its associated frequencies and/or aliasedsignals.

5) Collimation

With respect to the next technical variable of collimation, QA scoringis related to that of positioning. Improper collimation can result incertain critical areas of anatomy being excluded from the imaging study(excessive collimation) or result in too much anatomy being exposed anddisplayed on the image (insufficient collimation). If we were to take anabdominal radiograph as an example, improper collimation affects the QAscore to varying degrees based on the patient, clinical indication, andanatomic region of interest. If the patient is an elderly male patientpresenting with acute abdominal pain and the presumptive diagnosis ofpnemoperitoneum (free air), then excessive collimation of the upperabdomen precludes diagnosis and renders the exam non-diagnostic (QAscore “1”). If the same image is in an elderly male patient with renalcolic and questionable kidney stones, then the anatomic area of concernis not adversely affected by the excessive collimation and the entirearea of interest is included in the image obtained. As a result, thissimilar image (but in a completely different clinical scenario) wouldreceive a QA score of “3” (diagnostic).

In another example, a young female (age 18) with renal colic sent for anabdominal radiograph to evaluate kidney stones, would have an imagetaken in a similar manner to the previous examples, with the uppermostportion of the abdomen excluded, but visualizing the kidneys in theirentirety. However, while the clinical indication is similar, the patientprofile is different. A young female of child bearing age would warrantincreased collimation to minimize radiation exposure to the pelvis andgynecologic organs. The lack of collimation has therefore exposed thepatient to unnecessarily large amount of radiation which is a safetyconcern, which must also be factored into the QA analysis. As a result,the QA collimation score is “2” (limited), but does not require theimage to be retaken since the anatomic region of interest and clinicalindication were adequately evaluated.

In one embodiment consistent with the present invention, computerizedpattern recognition (see step S506) could be used to resolve the problemof the human body being, by definition, symmetrical. If one half of theanatomic region of interest is not visualized in its entirety, then thecomputer program 110 could easily identify the missing anatomy and alertthe technologist by display or alarm, as to the specific area notvisualized (see step S509). This is a common problem for radiographicand mammographic studies, resulting in “cut off” of the axilla on themediolateral mammographic image and costophrenic angle on frontal chestradiographs. This relatively simple deficiency is a common cause ofretakes and can be easily ameliorated with use of computerized patternrecognition software to compensate for it.

6) Supporting Data

Finally, the last technical variable is supporting data, which is anextremely important, yet overlooked, QA variable. The previous exampleshave illustrated the clinical importance of supporting data to QAanalysis, in addition to interpretation. Patient demographics, pertinentlaboratory and clinical testing data, history and physical examfindings, and clinical indication are all essential components inmedical image optimization. All components of the QA imaging cycle areaffected by this supporting data including the exam selection, definingthe optimum acquisition protocol, post-image processing, andinterpretation. While insufficient supporting data will not directlyrequire the image to be repeated, it may require the performance of anadditional imaging exam.

In one example, three patients are referred for skull radiographs, allin the setting of trauma. The first is an otherwise healthy 35 year-oldmale without pre-existing medical history who tripped and struck hisforehead, and incurred minimal soft tissue swelling with no loss ofconsciousness. The second patient is a 70-year old male with a historyof repeated falls, who is currently taking anti-coagulation medication(i.e., “blood thinners”) due to past history of deep venous thrombosis(i.e., blood clot). The third patient is a 6 year-old male with nosignificant past medical history, but presents with several unusualbruises on his extremities and torso.

If this supporting data was available prior to performance of theserequested skull radiographs, two of the requested studies would bechanged. The requested skull radiographs to evaluate the first patient(healthy 35-year old male) would be sufficient (assuming the exam isnormal). Appropriate imaging evaluation of the second patient (70-yearold male with repetitive trauma on anti-coagulation therapy), wouldconsist of CT in lieu of plain radiographs. This is because this patientis at increased risk for intracerebral hemorrhage, which would not bevisualized with the requested skull radiographs. In fact, a “negative”skull radiograph report would create a situation of false security andpotentially place the patient's life at risk. The third case (6 year-oldmale with suspected non-accidental trauma) would be concerning for childabuse and warrant a complete bone survey for appropriate diagnosis.Skull radiographs alone would be insufficient for proper diagnosis.

The addition or deletion of this supporting data is clearly important inmedical image management and diagnosis, and in the calculation of the QAscore (see FIG. 2A, step S511). Thus, the supporting data should beentered into the computer database, and this data can be automaticallyretrieved by the computer when the image is taken of the patient. Thus,the supporting data is incorporated into the QA analysis and scoring(see steps S501, 521), and feedback on the supporting data can bedisplayed by the computer program 110 (see step S523) for the benefit ofboth the referring clinicians and radiologists to improve clinicaloutcomes. Further, at the same time, supporting data from the computeror EMR can be automatically retrieved for each patient by the computerprogram 110, and the requested imaging exam can be based on definedprotocols and trending analysis (see step S522).

Scoring

The data obtained during the imaging study is also deposited into thecomputerized QA database where it is stored for prospective andretrospective trending analysis, so as to identify immediate andrecurrent problems, as they relate to equipment, personnel, data input,and overall workflow. The result of this automated technical QA analysisis that an automated and unbiased reject/retake analysis is created thatis no longer subject to human error and subjectivity.

During the image taking process, any variable which is determined tohave a deficiency that exceeds a pre-determined QA standard thresholdprestored in the database of the computer (see steps S513, S514, S519),will trigger the computer program 110 to have an alarm, or an electronicmessage instantaneously sent to the technologist alerting them as to thespecific type of deficiency and requirement for correction (see stepsS515, S516).

For example, a chest radiographic image with motion rendering the examnon-diagnostic (QA score of “1”), would have an instantaneous messagesent in parallel to the console of the CR/DR image acquisition device,the chief technologist's QA worklist, and the comprehensive QA database113. If a repeat image is not resubmitted to the PACS within a definedperiod of time (e.g., 5 minutes), an urgent text message is sent to theradiology administrator notifying them of the oversight (see step S515).These “lack of resubmissions” are documented in a separate QA databasefor continued monitoring and analysis (see steps S517 and S518).

In addition, the individual technical variable scores are automaticallytallied and recorded in the QA database (see FIG. 2B, step S520). Thesecan be cross-referenced by the computer program 110 to a number ofindependent variables including the exam type, patient characteristics,modality, equipment, day/time of exam for trending analysis (see stepsS521 and S522).

In one embodiment, the technical acquisition parameters obtained by thecomputer program 110 during the taking of the radiographic image, can bestored with the medical imaging file in the DICOM header (i.e., computerdatabase 113) for future retrieval and QA review, analysis, and futureuse (in follow-up exams).

In another embodiment consistent with the present invention, thesetechnical acquisition parameters can be correlated automatically by thecomputer program 110, with calculated QMDI's and technologist profiles(individual and group) to provide educational and training feedback (seestep S523). Specifically, the technologist profile may include, forexample, the type of position, the type of radiographic images taken,and a percentage of images taken with a high/low Likert score, etc. Thecomputer program 110 would keep tally of the Likert scores obtained withrespect to a particular technologist or group, and if a particulartechnologist takes images which are consistently low in Likert scores(i.e., the percentage of low Likert scores is high), then training forthat technologist may be necessary.

In another embodiment consistent with the present invention, thetechnical acquisition parameters, may be profiled by the computerprogram 110, with the best QA scores (for each modality, anatomicregion, and individual patient, for example), and the technicalacquisition profile can be automatically displayed by the computerprogram 110 to the technologist as the standard default (see steps S505,S601) prior to performing the next exam on that same patient. Thisshould theoretically improve image quality, interpretation accuracy, andpatient safety (by minimizing radiation dose with repeat and additionalimages).

With respect to another embodiment consistent with the presentinvention, the computer program 110 can perform prospective outcomesanalysis by correlating existing mammography statistics (i.e., truepositives, true negatives, false positives, and false negatives)obtained from the imaging, with stored pathology reports, follow-upimaging exams, and QMDIs. The prospective outcome analysis (see stepsS521, S522, S523) should provide the means to document a direct causeand effect relationship between improved QA measures (e.g., motion) withdiagnostic accuracy, economics, and clinical outcomes.

This electronic QA database 113 can be accessed at any time by theindividual technologist, supervisory technologist, department/hospitaladministrator, or chief radiologist to review individual and collectiveperformance of technologists, technology (e.g., CR devices), and examtypes. The trending analysis provided by this data can in turn be usedfor educational purposes, performance review, and new technologydeployment (see step S522).

Thus, based on the cumulative data of these technical QA variables, anoverall technical QA score is calculated (see step S511) and recorded bythe computer program 110, into the QA database for trending analysis(see steps S520, 522). The trending analysis is performed automaticallyby the software program 110, which can be customized to the specificneeds and concerns of the department (see step S522). This data can alsobe pooled to provide objective and reproducible comparison data withother imaging departments with similar technology, patient, and clinicalprofiles (see step S523). These national data can in turn be used toacknowledge those centers of excellence which demonstrate high levels ofQA proficiency and continued quality improvement (see step S526).

As stated above, a reject-retake analysis is also calculated todetermine the frequency of image rejections and required retakes as itrelates to each individual technologist and the collective group (seestep S513). The reject/retake analysis is automatically calculated bythe computer program 110, from the computer-derived QA scores andanalysis (see step S513). Any exam which has exceeded the pre-defined QAthreshold calling for the image to be repeated would be tallied as a“reject/retake”. The corresponding data associated with this rejectedimage (e.g., technologist, day/time of exam, equipment used, exposureparameters, location, patient profile, clinical indication, etc.), wouldbe recorded by the computer program 110, to provide future insight as todeficiencies that can be monitored and improved upon in the future (seestep S520). This provides important feedback for educational andtraining purposes, for ongoing quality improvement efforts.

A few relevant examples to illustrate these technical QA variable scoresare as follows:

One of the most common medical imaging studies performed is the chestradiograph, which provides diagnostic information about a number ofimportant anatomic structures within the thorax, as well as a largenumber of clinical entities. The anatomic structures included in a chestradiograph include (but are not limited to) the bony skeleton (ribs,sternum, spine), chest wall, heart, pulmonary and great vessels (aorta,superior vena cava), lung fields, pleura, hila and mediastinum, andupper abdomen. The clinical entities that can be evaluated include anumber of different pathologic states associated with each of theseanatomic structures, along with evaluation of inserted man-made devicessuch as a cardiac pacemaker, thoracostomy tube, or central venouscatheter.

In our example, we will evaluate QA scoring for two post-operativepatients, with respiratory distress, and concern for underlyingpneumonia.

Similar analyses and differences in QA scoring can be illustrated forthe other technical QA variables, using chest radiography as the imagingstudy. Positioning expectations for the portable ICU and standing chestradiographic images are far different. Due to differences inmagnification, technique, and ability to manually adjust each patient,significant QA scoring differences exist for these two examinations. Aportable ICU chest radiograph will commonly limit complete visualizationof the chest wall and upper abdomen. For the clinical indication of“cough, rule out pneumonia”, this is not a clinically significantcompromise for the areas of clinical concern (i.e., lung fields) arevisualized in their entirety and would therefore receive a QApositioning score of “3” (diagnostic). If, this same portable chestradiograph had a different clinical indication of “trauma, rule out ribfracture”, the QA positioning score would be change from a “3”(diagnostic) to “2” (limited). This is because the primary area ofclinical concern (ribs) is part of the chest wall, which wasincompletely visualized. Depending upon the degree of chest wallvisualization and specific area of symptomatology, this QA positioningscore could be further downgraded to a “1” (non-diagnostic). If forexample, the area of chest wall that was partially obscured iscontralateral to the area of clinical concern, the QA positioning scorewould be “2” (limited), since the primary clinical question can still beanswered. If on the other hand, the area of partially obscured chestwall corresponds directly to the anatomic region of clinical concern,the QA score becomes a “1” (non-diagnostic) due to the inability toaccurately detect the presence or absence of rib fracture. Thisillustrates the fact that QA scoring is dependent upon multiplevariables including the technical deficiency, patient profile, clinicalindication, and technique employed.

B. Radiologist QA Data

The single most important component of radiologist QA data recorded intothe QA database 113 is subjective measures of image quality, which arereported for each imaging exam interpreted as part of the routineworkflow using PACS 30. For each exam reviewed, a radiologist isprompted by the computer program 110 by means of a pop-up menu, forexample, which requires the radiologist to provide the following data:

1) Overall subjective image quality scale

-   -   1—Non-diagnostic    -   2—Limited    -   3—Diagnostic    -   4—Good    -   5—Exemplary

For exams with a reported image quality score of ≦2, a mandatory fieldis required to identify the limiting factor/s, as follows:

-   -   1—Motion    -   2—Positioning/Collimation    -   3—Exposure    -   4—Artifacts    -   5—Image Processing    -   6—Protocol Employed    -   7—Supporting Data    -   8—Other (Explain) ______

2) Clinical efficacy (appropriateness) of exam performed

-   -   1—Appropriate based on clinical indication provided    -   2—Uncertain, due to insufficient clinical information    -   3—Inappropriate, exam deemed unnecessary    -   4—Inappropriate, alternative imaging study preferred (Specify        Preferred Imaging Exam ______)

In addition, radiologist and exam-specific data is recorded by manualentry and by the computer program 110, respectively, into the QAdatabase 113 to include the specific types of image processing utilized,image manipulation (e.g., window/level adjustment, multi-planarreconstruction), and supporting patient demographic, imaging, andclinical data accessed from the RIS 20, PACS 30, and EMR. These data arerecorded as a function of the individual radiologist, exam type(anatomic region and modality), clinical indication, and patientprofile. It provides valuable information that can be cross-referencedby the computer program 110 with clinical outcome data (in the forms ofdischarge diagnosis, operative/procedure notes, pathology reports, etc.)to determine which data points (and radiologist profiles) are mostinformative and accurate in achieving diagnostic accuracy. These datacan in turn be directly embedded into radiologist workflow by thecomputer program 110 (see FIG. 4, step S708), so that each radiologist'sprofile is quantified and “best practice” templates are created based onthe exam type, clinical indication, and individual radiologistpreferences (see step S709).

Pertinent examples of how this strategy would be employed are asfollows:

In one example, a radiologist is reviewing a brain MRI with the clinicalindication of acute onset of right arm weakness. Based on theestablished image acquisition and interpretation protocols for “stroke”,the radiologist is presented with conventional spin echo sequences inorthogonal planes, supplemented by specialized diffusion and perfusionimages, and MR angiography (see steps S700, S701). The individualradiologist has an identified preferred display hanging protocol using a4 on 1 format, with axial spin echo and diffusion images displayed bythe computer program 110, side by side on the left hand monitor andhistorical comparison CT and MRI examinations displayed on the righthand monitor, with comparable MRI sequences anatomically linked.

Window/level settings are automatically adjusted by the computer program110, to the radiologist's preferences (see step S702) with reconstructedMR angiographic images displayed in axial and coronal planes. Thepatient's medical, laboratory, and imaging files are automaticallyqueried (using artificial intelligence techniques) by the computerprogram 110, to provide the radiologist with a synopsis of the patient'spast medical and surgical history (which includes a prior right carotidendarterectomy and old right hemispheric infarct), outside report ofcerebral angiography, and recent transcranial ultrasound (see stepsS700, 701).

An example of how this might apply to a chest radiographic image, wouldbe two radiologists—A and B—who have different preferences for theexposure of the image. The radiologists would be independently reviewingthe same standing chest radiographic image on a patient with alongstanding smoking history being screened for lung cancer. RadiologistA would prefer his/her image to be slightly under-penetrated (i.e.,lighter), while radiologist B would prefer his/her image to appear moreover-penetrated (i.e., darker). When the radiologist name oridentification number etc., is inputted into the computer system 101 atthe start of the image taking process, the program 110 wouldautomatically search its database 113 for predefined user-specificpreferences, such as preference for exposure (see step S600). Thus,after the image is taken, the computer would automatically apply thepredefined preferences in post-processing to the image (i.e., byadjusting the look up table), and each image can be displayedindependently by the program 110, providing each user with the idealized“look and feel” that they desire (see step S702). In each case, thetechnologist-selected image acquisition parameters are the same, but theappearance of the image when displayed for interpretation by the program110, varies for each individual radiologist. While these post-processingtechniques will not alter the computer-generated technical exposure QAscore, it would allow for enhancement of category “2” (limited) images,which may obviate the need for repeating the image.

In another example, another radiologist is reviewing a chestradiographic examination with the clinical indication “shortness ofbreath” (see steps S700, S701). Based on the specific radiologist'sprofile (see step S702), the images are automatically processed by thecomputer program 110, and displayed for the radiologist for review (seestep S703). Additional history obtained from the EMR, when queried bythe user, and prior imaging report archives, relate a previous diagnosisof chronic interstitial lung disease (see step S704). The correspondingCT exam is automatically retrieved by the program 110, from thepatient's electronic imaging folder and pre-selected “key images” chosenby the computer program 110 (see step S704), are displayed alongside thechest radiographic images to be interpreted. Based on this additionalhistory of “interstitial lung disease”, the computer program 110automatically applies a specialized image processing algorithm (edgeenhancement) to highlight the interstitial lung disease, and displaysboth the “standard default” and “processed” images next to one anotherfor review by the user.

In another example, a radiologist interpreting a screening digitalmammogram in a high-risk patient (history of maternal breast cancer) hasthe images displayed in the customary fashion, with side by sidecomparisons of current and historical comparison images. Based on the“best practice” template (see step S709), the computer program 110automatically provides a computer-aided diagnosis (CAD) overlay (seestep S710), which the radiologist can turn on and off using the inputmeans 104. When the CAD program 110 identifies a focal area ofsuspicious microcalcifications, the computer program 110 automaticallymagnifies this area on both the current and historical comparisonstudies, allowing for temporal comparison at a higher resolution. Theradiologist reports this as a BIRADS category 4 (suspicious formalignancy) in the database 113 and issues a stat report to thereferring clinician with recommendation for surgical biopsy (see stepS711). An electronic message is simultaneously sent by the computerprogram 110, to automatically retrieve the surgical pathology report atthe time of recommended biopsy and link the mammography and pathologyreports for direct correlation (see step S712).

C. Vendor QA Data

In order to automate the overall medical imaging QA process, it isimperative that routine quality control (QC) be employed on theacquisition and display devices. This can be done through the use of QCphantoms which are directly embedded into technologist and radiologistworkflow to ensure that quality assurance is maintained at the points ofimage acquisition, display, and interpretation.

A Quality Control (QC) phantom 32 for digital radiography is a deviceconstructed from sheets of copper, aluminum, acrylic, and otherattenuators, with embedded resolution test patterns, step wedges, andfiducial markers that provide a means to measure and verify theperformance characteristics of the x-ray system and x-ray detector (both21). The phantom 32 may be part of the vendor's components to testsystem-specific characteristics, or may be a “third-party” phantom tomeasure and verify system-independent performance characteristics. Agood QC phantom “system” provides an object that is conducive to easyhandling and positioning, allows the use of typical x-ray acquisitiontechnique factors for common imaging procedures, requires only one ortwo x-ray image acquisitions with minimal user interaction, uses acomputer program 110 to analyze phantom images with automatic softwarealgorithms, log data results, plot trends and alert the user when thesystem is outside of tolerance limits.

System performance measurements within the QC phantom 32 include but arenot limited to the following:

Spatial resolution assessment using a computer program 110 to performqualitative evaluation of bar phantoms or quantitative evaluation of anedge spread or line spread tool to generate a modulation transferfunction (MTF) in the center of the image as well as peripheral areas.Acquisition of data with magnification addresses geometric blurring bythe focal spot.

Contrast resolution assessment using a computer program 110 to performqualitative evaluation of decreasing diameter and decreasing thicknessdisk attenuators (contrast-detail section), quantitative measurement ofcontrast to noise ratio using automated region-of-interest (ROI)analysis.

Dynamic range measurement and system exposure linearity response isperformed by a computer program 110 using a calibrated plastic stepwedge phantom projection with ROI measurements. This demonstrates thecapabilities of the detector 32 to capture a wide range of exposures.

Distance measurement accuracy and detector element size calibrationverification using the fiducial marker image projections of a knowndistance (for a given magnification factor) in horizontal, vertical andoblique directions in the image, is performed using a computer program110.

Peak kilovolt (kVp) assessment is performed by a computer program 110,using differential transmission measurements through copper filterthicknesses; half-value-layer (HVL) determination from evaluation ofaluminum step wedge transmission signals.

Image uniformity and signal to noise ratio (SNR) evaluation of a “forprocessing” image is performed by a computer program 110 using ROIanalysis of central and peripheral values of a uniformly exposedconstant thickness object, and verification of flat-field correctionalgorithms using noise power spectrum (NPS) quantitative algorithms.

Artifact evaluation/analysis tests performed by the computer program110, employ uniformity images in 6) above, for evaluation. Inparticular, bad detector element mapping, column and row defects beforeflat-field correction, image retention artifacts, etc. are evaluated bythe computer program 110, using algorithms to seek high and low valuepixel defects, clumps, vertical and horizontal lines, and all signalsabove or below a threshold level in the image.

Each test (1-7) is automatically performed by the computer program 110(see FIG. 5, step S800) on the image data, logged and stored withrespect to the date of acquisition. All QC image data is achieved in the“for-processing” format by the computer program 110, for qualitative andquantitative re-evaluation when necessary. Immediate feedback to thetechnologist is provided by the computer program 110, on the imagereview workstation as a color-coded response of green (acceptable, readyfor patient imaging)—yellow (patients can be imaged, but attention tosystem calibration/repair is needed)—or, red (equipment MUST be repairedprior to patient imaging) for each of the phantom subsections. Anoverall green-yellow-red is also displayed, with 2 or more yellowwarnings eliciting an overall red (or whatever is deemed appropriate)(see step S802).

In addition to the automatic feedback, the computer program 110 canstore and use more detailed information of trend analysis of themeasured parameters for the prediction of detector 32 or other systemfailures before they occur, allowing preventative maintenance repairsbefore the system becomes inoperable (see FIG. 2B, steps S522, 523).

In one embodiment, depending on the specific system, the exposure indexcalibration can also be performed by the computer program 110 (see stepS802), using known incident exposures to the detector 32 and verifyingthe exposure index value accuracy reported by the digital detector 32.As this exposure reporting value is currently vendor specific, analgorithm adjusted to the vendor's methodology used by the computerprogram 110, can provide a means to determine the accuracy of thereported values, and an ability to give feedback information to adjustthe exposure index accuracy of the digital imaging system (see stepS803).

The exposure index represents a value proportional to the overallincident exposure to the detector 32, and therefore to the signal tonoise ratio (SNR) and to image quality. However, a need to keep theradiation dose as low as reasonably achievable to the patient pushes thedetector exposure to lower values. Optimization attempts to achieveappropriate “disease-specific” image quality at the lowest patient dosepossible. Since the SNR is proportional to the square root of thedetected exposure measured by the detector 32 (assuming quantum noiselimited operation), then the SNR is proportional to the detectedexposure and related to the exposure index. In many digital detectors 32with interfaces to x-ray generators, the kVp (0018,0060), mA (018,1151),and exposure time (0018,1150) parameters are mapped to DICOM (tag,element) header values, as well as the geometry of the x-ray system(source to image distance and object to image distance). Thisinformation can be extracted by the computer program 110 to calculate avalue related to patient dose, and by calculating the SNR/patient dose,provides a unitless “figure of merit” (FOM) that can aide in theoptimization of specific imaging procedures and determination ofappropriate radiographic techniques (selection of kVp to maximize theFOM for the lowest possible detector exposure) (see step S607).

Additionally, because digital detectors 32 have a wide dynamic range andare very forgiving, particularly when overexposures occur (the imageslook good, but the patient dose is too high), the exposure indexgenerated by the computer program 110 can provide feedback to thetechnologist to avoid a “dose-creep” problem. Extreme overexposures willlead to signal saturation and a loss of information in over-penetratedareas of the image. Underexposures are also a problem, because theimages are too noisy for the intended diagnostic need. Both the exposureindex (proportional to SNR) and FOM values can be used by the computerprogram 110 to generate feedback not only to the technologist, but totechnologist administrators and to radiologists for tracking down imageproblems and to provide continuous quality improvement and betterpatient care (see step S523).

For both the technologist and the radiologist, the soft-copy displayquality and viewing conditions are extremely important. The QC phantom32 and software program 110 contains test images that verify qualitativeand quantitative image information transfer to the human viewindependent of the quality of the images generated from a digitalradiography system. A common test image phantom is the SMPTE testpattern, which drives the display system to the low-end dark and to thehigh-end bright ranges. Within the extreme dark and bright areas areinset boxes of 5% higher and lower digital values, respectively, to testthe ability of the display and video driver to appropriately render theinformation visible. This quick qualitative check by the computerprogram 110, determines whether the digital driving levels of the videocard are set properly; if not, calibration using the DICOM part 14Grayscale Standard Display Function (GSDF) should be performed, and thetest re-evaluated.

Additional areas in the image test performed by the computer program110, display uniformity, linearity, spatial resolution, and distortionby providing a uniformly bright area, variations in brightness intensityin linear steps from dark to bright, high contrast and low contrast testpatterns to demonstrate spatial resolution capabilities of the monitor,and a standardized grid pattern to test geometric distortion of thedisplay.

D. Clinician QA Data

An important and often overlooked component of any imaging QA program110 is the evaluation of imaging utilization, specifically as it relatesto established appropriateness criteria. Over-utilization not onlyresults in excessive cost but also has the potential to adversely affectpatient safety, due to the additional radiation exposure and potentialcomplications inherent to these inappropriate and unnecessary exams.Complications can take a number of forms including contrastextravasation, allergic reaction to contrast administration, andphysical injury to the patient (which is particularly common in thecourse of interventional procedures).

Utilization review by the clinician can be partly automated by accessingthe QA database 113 for radiologist assessment of clinical efficacy (aspreviously described), as well as cross-referencing the clinicalindication and patient history (which are contained within the EMR) withestablished imaging criteria for appropriateness, which is currentlyavailable in existing computerized physician order entry (CPOE)programs, to generate reports (see step S706) which are stored in thedatabase 113 (see step S707).

These CPOE programs also require adequate clinical input at the time ofexam ordering, thereby assisting the imaging QA process. Thecomprehensive data contained within the QA database 113 can in turn beused to generate reports for clinician education and training, to ensurethat imaging utilization is appropriate (see step S523). Clinicianprofiles can be generated by the computer program 110, that identifythose types of exams or specific physicians that tend to misappropriateimaging services.

E. Patient QA Data

Despite all of the attention and scrutiny placed on patient safety, fewsafeguards currently exist within most medical imaging departments todocument and analyze potential adverse outcomes, as they relate tomedical imaging. If for example, a patient was to incur an allergicreaction to intravenous contrast administration, there is noreproducible method within the RIS 20 or PACS 30 that reproducibilityalerts radiology personnel prior to future studies. This is particularlytroublesome in the environment when patients often seek medical servicesat different institutions and often provide incomplete histories.

In the present invention, the imaging QA database 113 provides the meanswith which to document all relevant medical history that may serve toalter medical imaging delivery.

This can take a number of different forms including contraindications tospecific imaging studies (e.g., cardiac pacemaker for MRI), previousallergic reactions to medications or contrast agents, or medicalconditions that preclude administration of iodinated contrast (e.g.,renal insufficiency) (see step S501).

As previously mentioned, one of the most commonly overlooked areas ofpatient safety that can be prospectively improved upon is the radiationexposure experienced with imaging exams that utilize ionizing radiation.By tailoring the exam type and protocol to the specific patient and theclinical indication, radiation can be effectively reduced withoutcompromising exam quality. The imaging QA database 113 can recordnumerous data points to facilitate this radiation dose optimizationincluding:

Exposure parameters (and calculated radiation dose) for each individualexam;

Cumulative radiation exposure for each patient;

Previous imaging exams' exposure parameters and the corresponding imagequality scores (this provides optimum exposure parameters and reducesthe need for retakes); and

Clinical indication and past medical history which allows for tailoreddose reduction protocols, specific to the clinical question at hand.

F. Administrative QA Data

The compilation of data contained within the imaging QA database 113provides the means for supervisory and administrative staff to use thecomputer to generate reports so that they can systematically review thevarious profiles on technologists, radiologists, clinicians, andtechnology. By performing trending analysis on this data (see step S523)using the computer program 110, administrators can develop a betterunderstanding of how imaging department services and practitioners canimprove QA and the existing limitations. These QA profiles encompass anumber of predetermined stakeholders and perspectives and includetechnical deficiencies in image acquisition, safety concerns inherent toionizing radiation, physician compliance (as it relates to bothradiologists and referring clinicians) and technology concerns relatingto image acquisition devices, technology integration, and software usedin the overall QA process.

Personnel performance is an important component of the automated QAprogram 110 and is intended for educational and training purposes, inorder to facilitate improved workflow and end-quality (see step S523).For the technologist (who is the sole individual responsible for imageacquisition), performance evaluation consists of a number ofmeasurements including (but not limited to) the following:

Specific protocols used (as it relates to the clinical indication,technology used, patient profile, and supporting clinical data).

Exposure parameters (as it relates to the patient profile, technology,and clinical indication. In this particular situation, the patientprofile consists of physical characteristics (e.g. body habitus),clinical status (e.g., ambulatory versus non-ambulatory), and radiationprofile (e.g., cumulative radiation exposure).

Exam-specific QA characteristics (which are specific to the anatomicregion surveyed, imaging modality used, and clinical indication.

One example of exam-specific QA characteristics would be a pediatricelbow radiographic exam performed to evaluate trauma. In this particularpatient population, the identification of a non-displaced fracture isoften dependent upon the indirect radiographic sign of fat paddisplacement, as opposed to direct visualization of a fracture. In orderto optimize detection of a non-displaced fracture in this specific typeof exam, a properly positioned true lateral view is mandatory. Improperpositioning results in inaccurate detection of the fat pads, which inturn can lead to erroneous diagnosis.

4) Overall subjective image quality, as judged by the interpretingradiologists and clinicians.

This image quality score is not intended to be a “beauty contest” orfacsimile to the standard film image, but instead a measure of theimaging examination's ability to detect pertinent pathology. In additionto the comprehensive image quality score, physicians would be promptedby the computer program 110, to provide the specific variables thatlimit overall quality in the event of a “low” image quality score (aspreviously discussed).

Physician compliance data (for both radiologists and referringclinicians) would also be an important component of the administrativeQA data review. This data would record (and provide a physician-specificand group profiles) a number of important variables that contribute tomedical imaging QA such as:

Physician compliance to image quality scoring and identification ofdeficiencies;

Physician education and consultation (as it relates to ordering,decision support and communication) of medical imaging studies. For theradiologist this may entail documentation of results reporting ofemergent or clinically unexpected findings. For the clinician, this mayentail electronic auditing (and documentation) of on-line education,review of imaging data, and recording of clinically relevant informationto supplement exam ordering and interpretation. This would also includedocumentation of patient consultations (by both clinicians andradiologists) as it relates to safety concerns.

3) Utilization review (in conjunction with data obtained from the CPOEsystem), that allows for analysis of the appropriateness of examordering and the timely delivery of imaging services;

4) Physician compliance to QA standards and regulatory policies. Thiscan take the form of institution-specific, community-wide, andgovernmental QA regulations. Examples of existing policies that overseeQA include HIPAA, JCAHO, and MQSA.

Technology/vendor issues include those hardware and software entitiesthat are responsible for the various steps in the medical image chainincluding acquisition, processing, transmission, storage, display,interpretation, and reporting. Preventative maintenance and servicelogs, software upgrades, and equipment downtime are all incorporatedinto this component of the administrative QA record. Integrating theaforementioned technical and clinical QA data using the computer program110, into institutional information systems also requires integration ofthe imaging modalities with PACS 30, RIS 20, HIS 20, and EMR. Existingindustry-wide standards (HL-7, IHE, DICOM) currently exist to definethese standards and track industry compliance.

In addition, “digital dashboards” are commercially available softwareprograms that allow the administrator to continuously monitor and surveydepartmental workflow and equipment malfunction (see FIG. 2B, stepS524), which in essence creates an ongoing technology QA log. While thevarious components required for this comprehensive administrative QAprogram 110 exist (in one form or another), no entity to date hasintegrated these disparate data points into a single, all inclusivesystem for automating QA surveillance, as in the present invention.

In operation, the present invention includes the following steps asshown in the FIGS. 1-4. However, it would be well known to one ofordinary skill in the art that the steps may be combined, carried outsimultaneously, carried out alternatively, or carried out in a differentorder, or with the omission of some steps, or in difference sequences,as long as the intent of the present invention is carried out.

Accordingly, in the method of the present invention, the radiologistturns on the client computer system 101, which may be a stand-alone PC,or part of or connected to a client workstation known in theradiological field as the PACS workstation 30. In this exemplaryembodiment, the client computer 101 is the PACS 30, and some or all ofthe present invention, with respect to imaging display device 102,computer memory 109 and program 110 etc., is contained within the PACS30 instead of being provided separately.

Thus, the user logs onto the PACS system 30 once the client 101 isoperational.

The computer program 110 will then offer the user a menu directed toimaging studies which the technologist can then select, and the program110 will open up in the worklist folder listing image files availablefor analysis, from the menu offered (see FIG. 2A—step 500).

The radiologist can select and the computer program 110 will select anew imaging study, or can also load a previous imaging study (i.e.,patient data), including but not limited to image data corresponding toradiological examinations of patients from the PACS 30, and additionalsupporting information, including but not limited to laboratory data,pathology reports from the Electronic Medical Record (EMR), patientdemographics, administrative QA information, clinician and radiologistprofile information, etc., from data storage 113, onto the display 102.Note that the PACS 30 stores information according to existing standardssuch as DICOM. The data from the PACS 30 is stored in an examinationimage storage device 113 for example, where it can be accessed via theclient computer 101 for display on the image displaying device 102.Alternatively, the quality assurance system 100 of the present inventioncan directly access the PACS images in storage 114 without the need foran intermediate storage device 113, for example.

The selected imaging study—including all of the associated unread (orread) examination files for that patient, if any—is displayed by thecomputer program 110 on the display 102 of the client 101 in step S501.The study can be organized by the computer program 110 by DICOM seriesprior to display.

When the study only contains a few images (radiography or mammography),the radiologist reviews the images in a static fashion. If the imagingstudy contains many images (CT, MRI), the images are reviewed in adynamic fashion using a cine format (which is akin to a movie where theimages rapidly scroll up and down in a sequential fashion).

In step S502, the patient is hooked up to any of the devices 23, 24, 31,32 associated with obtaining a radiographic image, and positioned fortaking the radiographic image. In step S02, the technical variablesinput which collect data from the patient and the radiographic device21, is received from sensors 23, phantom devices 24, reflective devices31, and QC phantoms 32 (if any or all are present).

With respect to the issues of motion and positioning, the method of thepresent invention includes generating QMDI scores from the motionsensors 23, phantom devices 24, and reflective devices 31, in step S503.Once the QMDI scores are generated, a QA score is calculated based onthe input from these devices in step S504.

In an another embodiment, the computer program 110 may retrieve an idealexamination imaging study from the database 113 (step S505), and performa comparison and analysis of the images to determine if there have beenany changes in positioning or any issues of collimation (step S506).

Using this analysis, a positional and collimation score can bedetermined by the program 110 is step S507. If a change needs to be madein the position of the patient due to the score (S508), the program 110will notify the user in a display, message or alarm (step S509). In thatevent, the technologist will change the position of the patient and thenthe new input from the sensors 23, 24, 31 will be received to restartthe comparison/analysis process of steps S502, and S505-507.

If the position and collimation score is acceptable according to thepredefined limits in the database 113, as determined by the computerprogram 110, then the QA score is calculated based on the technicalvariables inputted into the system 101 (step S504).

Based upon the QA score calculated from the motion sensors 23, 24, 31etc., the program 110 can determine whether to recommend further changesto the technical variables (i.e., motion sensors 23, 24, 31), orposition of the patient etc. (step S510). As stated above, this wouldentail the technologist changing the position of the patient or theposition of the sensors 23, 24, 31, which will result in new input beingreceived in step S502.

After the imaging study has been taken, the QA score is calculated bythe program 110 (step S511) using information/supporting data etc.,retrieved from the database 113 (step S512).

The QA score that is calculated, undergoes a reject/retake analysis(step S513). If the technical variables exceed a certain threshold,and/or if the QA score is 1 or 2, that means that there is an issue withthe imaging study, such as with the position, motion, collimation, etc.(step S514), and the program 110 will transmit a warning to the user etal., in step S515, to discard the image, correct the problem and repeatthe imaging study (step S516).

If the imaging study is not retaken within a certain period of time(step S517), then this error is stored for future trending analysis(step S518), and another warning transmitted (step S515) until theimaging study is retaken (step S517) and the process restarted.

If the QA score is 3 or 4 (step S519), then the imaging study need notbe performed again, the examination is completed. The QA score is thenstored in the database 113 (see FIG. 2B—step S520), and is correlatedwith other QA information in the QA database 113, such that an analysisof the imaging study (step S521), as well as a trending analysis (stepS522) are performed.

This analysis is displayed in step S523 and recommendations foreducation, and feedback, are presented to the user. In addition, theprogram 110 will perform monitoring and analysis of QA issues and moregenerally, department workflow and QA (step S524).

QA data obtained from the analysis is also pooled with other imagingdepartments (step S525) and cross-referenced/analyzed, along with theresults of the monitoring and analysis of those departments (step S524),to result in recommendations for education and provide feedback to thoseusers and the departments (S526).

Along with the emphasis on the technical variables related to motion,position and collimation, FIG. 3 discloses the steps of the method ofthe present invention with respect to exposure parameters and theresolution of artifacts in imaging studies. The steps in FIG. 3 may beperformed concurrently with those in FIG. 2, or separately therefrom, orcertain steps can be performed separately or in combination, or omitted.

After opening the file for the imaging study as above in step S500, inone embodiment, the computer program 110 retrieves QA and exposureparameters from the database 113, in order to ascertain the exposureparameters for the individual imaging study (step S600).

In one embodiment, default exposure parameters are set by the program110 (step S601), which are used during the imaging study. Thereafter,the QA score is calculated based on the technical variables, as in FIG.2A, step S504 et seq.

In another embodiment, the program 110 has performed an analysis ofexposure parameters, and thus, sets predetermined (i.e., unique)exposure parameters (in step S602). Thereafter, the QA score iscalculated based on the technical variables, as in FIG. 2A, step S504 etseq.

In another embodiment, the technical variables related to the exposureparameters are received by the program 110 in step S603. However, if theexposure parameters retrieved from the database 113, and the settingsfor the new imaging study, would bring the cumulative exposure of thepatient over a predetermined limit stored in the database 113 (stepS604), the technologist would be warned and instructed by the program110 to use a low dose protocol or an alternative examination (stepS605). Thereafter, the QA score is calculated based on the technicalvariables, as in FIG. 2A, step S504 et seq.

If the cumulative exposure of the patient will not exceed thepredetermined limit, then, the technologist may set the exposureparameters such that the program 110 will receive and store thosetechnical variables (step S603).

In one embodiment, a PSNR analysis is also performed (step S606), butthis step need not be performed before the radiation dose exposure iscalculated in step S607. Once calculated, the exposure parameters arecorrelated with clinician and patient specific variables in step S608,before a QA score is calculated based on all the technical variables(see FIG. 2A, step S504). Thereafter, the method follows the samesequence as in steps S510 et seg.

In another embodiment, after the exposure parameters (i.e., technicalvariables) are inputted, the image taken in the study is cloned (stepS609), and a shift and subtract operation is performed in step S610.Thereafter, a quantitative analysis, and a noise spectrum analysis areperformed, among others, in step S611 to determine a final QA compositeimage, where any artifacts in the image may be isolated, or overlayed onthe image obtained (step S612).

Thereafter, the QA score is calculated based on the technical variables,as in FIG. 2A, step S504 et seq.

With respect to FIG. 4, the steps involved in the present method ofensuring QA in radiology with respect to radiologists and clinicians,may be performed concurrently with those in FIGS. 2 and 3, or separatelytherefrom, or certain steps can be performed separately or incombination, or omitted.

The steps include the program 110 retrieving information from the QAdatabase in step S700, after the program 110 opens the file for animaging study (step S500). QA information on each imaging study isdisplayed on the monitor 102 by the program (step S701), with theprogram 110 automatically adjusting the format of the display 102according to prestored radiologist preferences (step S702). The overallobjective image quality scale (i.e., QA scores) are displayed for theuser (step S703), and patient information and supporting data areretrieved in step S704 to provide additional information for theradiologist.

Once the radiologist has performed his analysis, he/she will input thefindings into the computer 101, on the image quality and clinicalefficacy of the image study (step S705). Thereafter, the clinician willreview the radiologist's findings (step S706), and the clinician'sreport will also be stored in the database 113 (step S707).

The radiologist's findings based on the QA scores etc., will be embeddedin the workflow for program 110 quality control analysis of theradiologist profile (step S708). The program 110 can utilize theinformation in the database with respect to each radiologist, andperform a trending analysis to produce a “best practice” template forall radiologists to follow (step S709). This “best practice” templatecan be provided as a CAD overlay in some embodiments (step S710).

These reports and templates are all stored in the database 113, andmessages and feedback are forwarded to other radiologists/clinicians,and departments in step S712. Thereafter, QA scores are calculated basedon the technical variables, as in FIG. 2B, step S521 et seq.

Finally, the steps involved in assuring QA of the radiographic equipment21, include those in FIG. 5. These steps may be performed concurrentlywith those in FIG. 2, or 3, or 4, or separately therefrom, or certainsteps can be performed separately or in combination, or omitted.

The QC phantoms 32 are positioned with respect to the radiographicequipment 21, and the data from the phantoms 32 are received by theprogram 110 and a plurality of sophisticated testing and analysis of thedata is performed (step S800).

Based upon the results of the testing and analysis, the program 110 willdisplay a coded response to the user for calibration and/or repair ofthe radiographic equipment 21 (step S801).

Further, an exposure index calibration based on the received exposureparameters (see FIG. 3, step S603), is performed by the program 110. Theprogram 110 will then display the recommended adjustment to theequipment 21 based on the exposure index (step S803). Based on theequipment adjustments, the program 110 will calculate the SNR (stepS804), and this information, which can be utilized by the program 110 tocalculate radiation dose exposure to the patient, is inputted into thetechnical variables received by the program in step S603 in FIG. 3.

Once the patient radiation dose is calculated (step S607), then feedbackis provided to the technologist/radiologist on QC issues with theequipment that affect the imaging study (step S521 (FIG. 2B)), theinformation stored and a trending analysis performed (step S522), andrecommendations provided for education, and feedback provided, to theclinicians, radiologists, and departments (S526).

It should be emphasized that the above-described embodiments of theinvention are merely possible examples of implementations set forth fora clear understanding of the principles of the invention. Variations andmodifications may be made to the above-described embodiments of theinvention without departing from the spirit and principles of theinvention. All such modifications and variations are intended to beincluded herein within the scope of the invention and protected by thefollowing claims.

1. A computer-implemented method for quality assurance in radiology,comprising: retrieving quality assurance and exposure parameters from adatabase of a computer system; receiving information on technicalvariables, including radiation exposure parameters, from radiographicequipment in the performance of an imaging study on said individualpatient; determining whether a cumulative exposure exceeds apredetermined threshold; and utilizing one of specialized relativelylow-dose protocols, imaging examinations without ionizing radiation,and/or analysis of future imaging examination requests, when saidcumulative exposure exceeds said predetermined threshold.
 2. Acomputer-implemented method for quality assurance in radiology,comprising: recording radiation exposure parameters from imaging studiesusing an imaging apparatus, and storing said exposure parameters in adatabase of a computer system; recording predetermined protocolparameters in said database; and cross-referencing said protocol andexposure parameters with clinical data on an individual patient,pertinent to examination indication, laboratory data, and diagnosis, inorder to make a comparison and analysis with said imaging studies, tothereby improve diagnostic accuracy and reduce radiation exposure byminimizing additional and unnecessary imaging studies.
 3. Acomputer-implemented method for quality assurance in radiology,comprising: performing a pre-scan of an anatomic region of a patient tocreate a pixel density map of said anatomic region, using said imagingapparatus; and performing a histogram analysis of a pixel density ofsaid pixel density map to compute optimum radiation exposure parameters,including both image quality and radiation exposure dose; and storingsaid optimum exposure parameters in said database and utilizing saidoptimum exposure parameters before said imaging study is repeated, toimprove at least image quality.
 4. The method of claim 3, furthercomprising: performing an existing peak analysis to noise (PSNR)analysis to evaluate a variability in pixel data to quantitativelymeasure an amount of noise contained within an image in one of saidimaging studies; and calculating an inflection point at which areduction in said radiation dose is achieved without clinicallysignificant degradation in said image quality.
 5. The method of claim 4,further comprising: correlating said one of said imaging studies withprevious imaging studies and examinations and patient information storedin said database.
 6. The method of claim 4, further comprising:analyzing said pixel density map obtained over said anatomic region inconjunction with said PSNR analysis to produce an automated correctionto provide a required diagnostic information at a lowest radiation doseto said patient.
 7. The method of claim 6, further comprising:performing an analysis of said imaging studies for use in technologisteducation and training, development of decision support programs, andpatient outcomes analysis.
 8. The method of claim 7, wherein saiddecision support programs are customized to end-user preferences,specific clinical indication and a patient anatomy being evaluated, andare automatically provided to a user.
 9. A computer system for qualityassurance in radiology, comprising: at least one memory which containsat least one program having the steps of: retrieving quality assuranceand exposure parameters from a database of a computer system; receivinginformation on technical variables, including radiation exposureparameters, from radiographic equipment in the performance of an imagingstudy on said individual patient; determining whether a cumulativeexposure exceeds a predetermined threshold; and utilizing one ofspecialized relatively low-dose protocols, imaging examinations withoutionizing radiation, and/or analysis of future imaging examinationrequests, when said cumulative exposure exceeds said predeterminedthreshold; and a processor for executing the program.
 10. A computersystem for quality assurance in radiology, comprising: at least onememory which contains at least one program having the steps of:recording radiation exposure parameters from imaging studies using animaging apparatus, and storing said exposure parameters in a database ofa computer system; recording predetermined protocol parameters in saiddatabase; and cross-referencing said protocol and exposure parameterswith clinical data on an individual patient, pertinent to examinationindication, laboratory data, and diagnosis, in order to make acomparison and analysis with said imaging studies, to thereby improvediagnostic accuracy and reduce radiation exposure by minimizingadditional and unnecessary imaging studies; and a processor forexecuting the program.
 11. A computer system for quality assurance inradiology, comprising: at least one memory which contains at least oneprogram having the steps of: performing a pre-scan of an anatomic regionof a patient to create a pixel density map of said anatomic region,using said imaging apparatus; and performing a histogram analysis of apixel density of said pixel density map to compute optimum radiationexposure parameters, including both image quality and radiation exposuredose; and storing said optimum exposure parameters in said database andutilizing said optimum exposure parameters before said imaging study isrepeated, to improve at least image quality; and a processor forexecuting the program.
 12. A computer-readable medium whose contentscause a computer system to execute instructions of a program, theprogram comprising the steps of: retrieving quality assurance andexposure parameters from a database of a computer system; receivinginformation on technical variables, including radiation exposureparameters, from radiographic equipment in the performance of an imagingstudy on said individual patient; determining whether a cumulativeexposure exceeds a predetermined threshold; and utilizing one ofspecialized relatively low-dose protocols, imaging examinations withoutionizing radiation, and/or analysis of future imaging examinationrequests, when said cumulative exposure exceeds said predeterminedthreshold.
 13. A computer-readable medium whose contents cause acomputer system to execute instructions of a program, the programcomprising the steps of: recording radiation exposure parameters fromimaging studies using an imaging apparatus, and storing said exposureparameters in a database of a computer system; recording predeterminedprotocol parameters in said database; and cross-referencing saidprotocol and exposure parameters with clinical data on an individualpatient, pertinent to examination indication, laboratory data, anddiagnosis, in order to make a comparison and analysis with said imagingstudies, to thereby improve diagnostic accuracy and reduce radiationexposure by minimizing additional and unnecessary imaging studies.
 14. Acomputer-readable medium whose contents cause a computer system toexecute instructions of a program, the program comprising the steps of:performing a pre-scan of an anatomic region of a patient to create apixel density map of said anatomic region, using said imaging apparatus;and performing a histogram analysis of a pixel density of said pixeldensity map to compute optimum radiation exposure parameters, includingboth image quality and radiation exposure dose; and storing said optimumexposure parameters in said database and utilizing said optimum exposureparameters before said imaging study is repeated, to improve at leastimage quality.